Abstract:

Most of the real-life problems involve uncertainty, which needs to be delicately integrated into the decision-making processes. In this talk, we present stochastic optimization techniques that provide different modelling approaches to address this issue depending on the available information. In particular, we present stochastic programming and distributionally robust optimization approaches that incorporate varying levels of information regarding the distribution of the uncertain parameters into the decision-making processes. These approaches will be illustrated over problems involving various application domains including energy systems and supply chains. We further discuss the value of prediction models in representing the uncertainty for the subsequent decision-making processes.